The answer is in this GitHub issue. The key is to ensure that you have the ipywidgets notebook extension enabled using the following command: jupyter nbextension enable –py widgetsnbextension For the old JupyterLab 2.0 you’ll also need to install the JupyterLab extension: jupyter labextension install @jupyter-widgets/jupyterlab-manager For the old JupyterLab 2.0 installing the JupyterLab extension … Read more
Try using tqdm.notebook.tqdm instead of tqdm, as outlined here. This could be as simple as changing your import to: from tqdm.notebook import tqdm EDIT: After testing, it seems that tqdm actually works fine in ‘text mode’ in Jupyter notebook. It’s hard to tell because you haven’t provided a minimal example, but it looks like your … Read more
You can wrap tqdm around the executor as the following to track the progress: list(tqdm(executor.map(f, iter), total=len(iter)) Here is your example: import time import concurrent.futures from tqdm import tqdm def f(x): time.sleep(0.001) # to visualize the progress return x**2 def run(f, my_iter): with concurrent.futures.ThreadPoolExecutor() as executor: results = list(tqdm(executor.map(f, my_iter), total=len(my_iter))) return results my_iter = … Read more
Use imap instead of map, which returns an iterator of the processed values. from multiprocessing import Pool import tqdm import time def _foo(my_number): square = my_number * my_number time.sleep(1) return square if __name__ == ‘__main__’: with Pool(2) as p: r = list(tqdm.tqdm(p.imap(_foo, range(30)), total=30))